Comparison between LQR and ANN Active Anti-Roll Control of a Single Unit Heavy Vehicle

Authors: Babesse Saad, Ameddah Djameleddine

Abstract:

In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: Rollover, single unit heavy vehicle, neural networks, nonlinear side force.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338564

References:

[1] H. Kim and Y. Park. Investigation of robust roll motion control considering varying speed and actuator dynamics. Mechatronics 14, 35–54. 2004.
[2] P. Gaspar, I. Szaszi and J. Bokor. Reconfigurable control structure to prevent the rollover of heavy vehicles. Control Practice Engineering 13, 699–711. 2005.
[3] D.J.M. Sampson. Active roll control of articulated heavy vehicle. PhD Thesis, University of Cambridge, 2000.
[4] R. C. Lin. An Investigation of Active Roll Control for Heavy Vehicle Suspensions. PhD thesis, University of Cambridge, UK, 1994.
[5] A. Ba-razzouk, A. Cheriti and G. Olivier, ″A Neural Networks Based Field Oriented Control Scheme For Induction Motor ″IEEE Industry Applications Society Annual Meeting New Orleans, Louisiana, October 5-9, 1997.
[6] Ghouili, J and Cheriti, A″ Induction motor dynamic neural stator flux estimation using active and reactive power for direct torque control.″ Power Electronics Specialists Conference, 1999. PESC 99. 30th Annual IEEE Volume 1, Issue, Aug 1999 Page(s):501 - 505 vol.
[7] A.Ciocan,”Contribution a l’Analyse et à la Commande d’un Véhicule Automobile”, PHD Thesis, University of Picardie jules verne-Amiens, 2005.